上海航天（中英文）2026，Vol.43Issue(1)：63-73,81,12.DOI:10.19328/j.cnki.2096-8655.2026.01.006
基于深度网络的多源卫星数据舰船目标融合跟踪
Deep Network-based Ship Target Fusion Tracking with Multi-source Satellite Data
摘要
Abstract
With the rapid development of satellite remote sensing technology,a single data source no longer meets the needs of ship target tracking.The fusion of multi-source satellite observation data can provide comprehensive and accurate Earth observation information,overcome the limitations of a single data source,improve the target tracking performance,and thus support accurate analysis and decision-making.In this paper,the observation data from space-based microwave radar,electronic reconnaissance satellites,and synthetic aperture radar(SAR)satellites are adopted to study how to effectively fuse data from multiple satellite payloads to achieve accurate tracking of ship targets.First,a data fusion method based on the convolutional neural network(CNN)and attention mechanism is proposed,which can effectively integrate data from different modalities to enhance the performance of the model in complex tasks.Then,a data association algorithm based on graph neural networks(GNNs)is proposed,which ensures the consistency and continuity of each target during the tracking process.Simulation validation is carried out with the simulated dataset generated by the ship automatic identification system.The results show that the method obtains good fusion accuracy and tracking stability in three ship distribution density scenarios of 5 km×5 km,10 km×10 km,and 20 km×20 km,and has high value for engineering applications.关键词
多传感器数据融合/目标跟踪/航迹关联/卷积神经网络(CNN)/图神经网络(GNN)Key words
multi-sensor data fusion/object tracking/trajectory correlation/convolutional neural network(CNN)/graph neural network(GNN)分类
航空航天引用本文复制引用
李鑫晟,张海超,吴楚泽,冯书谊,郝禹哲,李元祥..基于深度网络的多源卫星数据舰船目标融合跟踪[J].上海航天（中英文）,2026,43(1):63-73,81,12.基金项目
上海航天先进技术联合研究基金资助项目(USCAST2022-38) （USCAST2022-38）
